{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一题：对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "常用的就是线性图和直方图了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "#plt.plot(df[\"MEDV\"],color='blue',label='line')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二题： 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = df.corr()\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "#plt.savefig(\"house_coor.png\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第三题： 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "应该直接使用最小二乘线性回归就行了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第四题： 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化\n",
    "（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 （10分），并重新训练最小二乘线性回归、岭回归、和Lasso模型（30分）。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.218876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>3.117950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>3.575151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>3.538057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>3.616309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO    ...     RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  \\\n",
       "0  0.208015      0.3    ...         0      0      0      0      0      0   \n",
       "1  0.104962      0.5    ...         1      0      0      0      0      0   \n",
       "2  0.104962      0.5    ...         1      0      0      0      0      0   \n",
       "3  0.066794      0.6    ...         0      1      0      0      0      0   \n",
       "4  0.066794      0.6    ...         0      1      0      0      0      0   \n",
       "\n",
       "   RAD_8  RAD_24  MEDV  log_MEDV  \n",
       "0      0       0  24.0  3.218876  \n",
       "1      0       0  21.6  3.117950  \n",
       "2      0       0  34.7  3.575151  \n",
       "3      0       0  33.4  3.538057  \n",
       "4      0       0  36.2  3.616309  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "# 尝试对y（房屋价格）做log变换，对log变换后的价格进行估计\n",
    "log_y = np.log1p(y)\n",
    "# RAD的含义是距离高速公路的便利指数。虽然给的数值是数值型，但实际是索引，可换成离散特征/类别型特征编码试试。\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "\n",
    "#特征名称，用于保存特征工程结果\n",
    "feat_names = X.columns\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "ss_log_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "#y = ss_y.fit_transform(y.reshape(-1, 1))\n",
    "#log_y = ss_y.fit_transform(log_y.reshape(-1, 1))\n",
    "\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "#加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "\n",
    "\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249    26.2\n",
       "51     20.5\n",
       "151    19.6\n",
       "486    19.1\n",
       "235    24.0\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = fe_data[\"MEDV\"]\n",
    "\n",
    "X = fe_data.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "# 校验集数据划分 校验集比例为20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=2019)\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最小二乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE为: 25.6422\n",
      "RMSE为: 0.6256\n",
      "线性回归系数为: [-1.26230771e+01  5.54945609e+00  8.02048636e-02  3.55596347e+00\n",
      " -6.73300449e+00  2.28177072e+01 -9.40968204e-03 -1.64940646e+01\n",
      " -4.40133104e+00 -8.04972488e+00  4.22315453e+00 -1.91593056e+01\n",
      " -2.86364733e+00 -1.98229581e+00  9.67341836e-01 -8.17997539e-01\n",
      " -5.51198052e-01 -2.13227774e+00  2.56042198e+00  7.80574775e-01\n",
      "  4.03907789e+00]\n",
      "线性回归截距项为: 24.804434883950712\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# RMSE\n",
    "lr_error = metrics.mean_squared_error(y_test, y_test_pred_lr)\n",
    "lr_error2 = metrics.r2_score(y_test, y_test_pred_lr)\n",
    "\n",
    "print('RMSE为: %.4f' % lr_error)\n",
    "print('R平方为: %.4f' % lr_error2)\n",
    "\n",
    "print('线性回归系数为: %s' % str(lr.coef_))\n",
    "print('线性回归截距项为: %s' % str(lr.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳alpha值为: 0.1000\n",
      "岭回归系数矩阵为: [-11.99267022   5.38102161  -0.0302468    3.58563052  -6.52005746\n",
      "  22.5238826    0.02981304 -16.01722233  -4.2613859   -8.05029019\n",
      "   4.20511515 -19.1820305   -2.82324595  -1.89183494   0.99034585\n",
      "  -0.813976    -0.54041956  -2.12570739   2.52178962   0.80221068\n",
      "   3.88083769]\n",
      "岭回归RMSE为: 25.4122\n",
      "R平方为: 0.6256\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "print('最佳alpha值为: %.4f' % ridge.alpha_)\n",
    "print('岭回归系数矩阵为: %s' % str(ridge.coef_))\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "rid_error = metrics.mean_squared_error(y_test, y_test_pred_ridge)\n",
    "lr_error2 = metrics.r2_score(y_test, y_test_pred_lr)\n",
    "\n",
    "print('RMSE为: %.4f' % rid_error)\n",
    "print('R平方为: %.4f' % lr_error2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE为: 25.4122\n",
      "R平方为: 0.6256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "# 训练模型\n",
    "lasso = LassoCV()  \n",
    "\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "rid_error = metrics.mean_squared_error(y_test, y_test_pred_ridge)\n",
    "lr_error2 = metrics.r2_score(y_test, y_test_pred_lr)\n",
    "\n",
    "print('RMSE为: %.4f' % rid_error)\n",
    "print('R平方为: %.4f' % lr_error2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第五题： 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "线性回归的建模速度快，相似的数据会有相似的结果，但是对噪声敏感。\n",
    "岭回归使用L2正则化，具有最小二乘法的速度，可以降低过拟合，没有特征选择性。\n",
    "Lasso使用L1正则化，适用于维度很多的情况，具有特征选择性，因为L1范数的解具有稀疏性，这使得它可以与稀疏算法一起使用，这使得在计算上更有效率。"
   ]
  }
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